Automated system and method to prioritize language model and ontology pruning

ABSTRACT

A system and method for updating computerized language models is provided that automatically adds or deletes terms from the language model to capture trending events or products, while maximizing computer efficiencies by deleting terms that are no longer trending and use of knowledge bases, machine learning model training and evaluation corpora, analysis tools and databases

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/659,913, pending, which is a non-provisional patent applicationclaiming priority to Provisional Patent Application Ser. No. 62/748,639,filed Oct. 22, 2018, which are both hereby incorporated by thisreference in their entireties as if fully set forth herein.

BACKGROUND Field

Embodiments of the present invention relate to language models andontologies, and more particularly, to a system and method forautomatically prioritize language mode and ontology expansion andpruning.

Background

In speech recognition, a language model (LM) is a graph of probabilitiesassociated to word transitions from a known vocabulary, such as a wordlattice. Word embedding is the collective name for a set of languagemodeling and feature learning techniques in natural language processing(NLP) where words or phrases from the vocabulary are mapped to vectorsof real numbers. Some approaches to language model development includeterm frequency inverse document frequency (TF-IDF) and word similarity.For instance, vocabulary in the insurance domain is expected to differgreatly from vocabulary in the telecommunications domain. To create a LMfor use in a specific domain, texts are gathered from various sourcessuch as websites, chat logs, call logs, documentation, and other sourcesin that domain, but each such domain may use different terms or syntaxfor the same meaning. There is a need for a system and method toautomatically prioritize language model and ontology expansion andpruning.

BRIEF SUMMARY OF THE DISCLOSURE

Accordingly, the present invention is directed to a system and methodfor a system and method for automatically prioritize language mode andontology expansion and pruning that obviates one or more of the problemsdue to limitations and disadvantages of the related art.

In accordance with the purpose(s) of this invention, as embodied andbroadly described herein, this disclosure, in one aspect, relates to acomputer product comprising computer executable code embodied in anon-transitory computer readable medium that, when executing on one ormore computing devices performs a method of normalizing terminology andphrases within a language model for a language domain. The methodincludes receiving text from a plurality of platforms; determiningwhether the text includes a term in a stored data model; identifying ifa term that does exist in the data model appears in a new context;passing the term in the new context to a human for determination if theterm should be added to a training example in the new context forretraining the data model; if the term does not appear in the newcontext, checking the term for frequency of use in a known context andadding the term in the known context to the training example with a newpriority if the frequency has reached a predetermined threshold; andrecompiling the language model based on the term in context.

In another aspect, the disclosure relates to a method of adding terms toa language model based on use. The method includes receiving text from aplurality of platforms; determining whether the text includes a term ina stored data model; identifying if a term that does exist in the datamodel appears in a new context; passing the term in the new context to ahuman for determination if the term should be added to a trainingexample in the new context for retraining the data model; if the termdoes not appear in the new context, checking the term for frequency ofuse in a known context and adding the term in the known context to thetraining example with a new priority if the frequency has reached apredetermined threshold; and recompiling the language model based on theterm in context.

In another aspect, the disclosure relates to a method of removing termsfrom a language model based on use. The method includes receiving textfrom a plurality of platforms; determining whether the text includes aterm in a stored data model; if the term is in the data model,determining a frequency of use of the term in the text in a context inwhich the term appears in the data model; deleting the term in contextfrom a training example for the data model if the frequency of use fallsbelow a predetermined threshold; and recompiling the language modelbased after removing the term in the context from the training example.

Further embodiments, features, and advantages of the system and methodfor a system and method for automatically prioritize language mode andontology expansion and pruning, as well as the structure and operationof the various embodiments of the system and method for a system andmethod for automatically prioritize language mode and ontology expansionand pruning, are described in detail below with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, which are incorporated herein and form part ofthe specification, illustrate the system and method for automaticallyprioritize language mode and ontology expansion and pruning. Togetherwith the description, the figures further serve to explain theprinciples of the system and method for automatically prioritizelanguage mode and ontology expansion and pruning described herein andthereby enable a person skilled in the pertinent art to perform and usethe system and method for automatically prioritize language mode andontology expansion and pruning.

FIG. 1 is an example of a word lattice.

FIG. 2 illustrates an active learning process according to principlesdescribed herein.

FIG. 3 is a flowchart showing an example set of steps for performing amethod as described herein.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments of the system andmethod for a system and method for automatically prioritize languagemode and ontology expansion and pruning with reference to theaccompanying figures. The same reference numbers in different drawingsmay identify the same or similar elements.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the present inventionwithout departing from the spirit or scope of the invention. Thus, it isintended that the present invention cover the modifications andvariations of this invention provided they come within the scope of theappended claims and their equivalents.

To create a language model (LM) for use in a specific domain, texts aregathered from various sources such as websites, chat logs, call logs,documentation, and other sources in that domain. Once the texts areaggregated, LM construction toolkits such as the CMU [1], SRI[2], orIRST[3] are applied to the data. They extract the vocabulary used withinthe texts and the statistics of their use with other vocabulary, such asunigrams, bigrams, and trigrams. These statistics can then be used tocalculate a priori statistics of sentences that can be formed using theknown vocabulary, which are organized in a lattice. A word lattice is anacyclic directed graph with a single starting node and edges labeledwith a word and its corresponding probability of following the currentword in the source texts. By following a path through the lattice fromthe starting point to any particular node, the a priori probability ofthat series of words (i.e. a sentence) appearing in the domain specifictexts can be calculated. In the case of FIG. 1 , the subject phrase is“a conference is being recorded.” An example of algorithms as applied totraverse a word lattice can be found athttps://www.slideserve.com/kipling/an-evaluation-of-lattice-scoring-using-a-smoothed-estimate-of-word-accuracy,which is incorporated herein in its entirety as background information.

A different approach to modeling word usage in context is to constructvectors to represent each word in a N-dimensional vector space. Thesevectors are manipulated during training based on observing where termsoccur in the context of the surrounding terms. Terms that occur in thesame context are moved closer to alignment. Terms that do not occur inthe same context are moved further away. Once trained, the set ofvectors can be used to reason about the similarity of words byperforming vector arithmetic, such as measuring the distance between twopoints in the vector space. This approach is known as word embeddings[4], and is a way to group similar terms in a corpus together. Both theLM and word embedding approaches are unsupervised in that they requireno human effort to construct. The training algorithms are simply givenlarge training corpora and they use term positions and statistics withinthe corpora to build a model.

In contrast to models showing the statistical relationship between termsin a training corpora, data modeling approaches seek to define deeperrelationships between terms such as hierarchies and negations. For suchmodels there are two common structures used. The simpler form is ataxonomy, which is simply a tree of entities that form a hierarchy. Forexample, you could create a taxonomy of food where the entities areindividual food items such as cheddar cheese, peas, corn, apples, pork,skim milk, etc. You would then create low level classes of foods likered meat, white meat, all cheese, all milk, families of fruits andvegetables, etc. Then you group all of the specific individuals into theclasses they belong. Next you create higher level classes such as meat,fish, dairy, fruit, vegetables, etc. and group the classes of foods intothe higher level classes. Finally, you can create the top layers ofanimal products, and non-animal products and put them under the rootnode of food. In this way you have constructed a taxonomy of food thatyou can go from specific examples to more and more general classes byfollowing the tree backwards. You can also do simple reasoning likeparent-of or sibling-of relationships, and find the least commonancestor between two individuals, like animal products for milk andpork.

For many cases this tree structure is enough to model data and processit. But more complicated relationships, like multiple inheritance andapplying logical assertions, require storing data and meta data in agraph form. This is where ontologies come in. An ontology is a directedgraph with four primary components: individuals, classes, attributes,and relations. There are many more components possible like events andrestrictions as well. Ontologies allow for very rich data modeling withcomplex relationships and logical inferences about the data. There aremany ways to construct ontologies and several different syntaxes forexpressing and storing them. Taxonomies and ontologies typically requiresome human effort to construct. They may be seeded by some statisticalobservations from corpora, but the relationships between terms areusually defined or refined by humans. These models are concerned withthe logical inference that can be drawn from terms within them andtherefore require at least some logical relations to be encoded withinthem by humans.

Human-in-the-loop (HITL) is a subfield of Machine Learning where themodel requires some form of human interaction. A common HITL approach isknown as Active Learning. With active learning an existing model issupplied with a large pool or stream of unlabeled samples. The modelthen chooses which samples it thinks would be most informative to knowthe label for based on a selection strategies, of which there areseveral commonly used. Human oracles are then shown the selected samplesand give them labels. These labeled samples are added to the trainingdata to retrain the model from. In this way the model will learn morequickly from less training data then given a large sample of labeledresults that contain many duplicated features. This active learningprocess is shown in FIG. 2 .

Language model and ontology refinement for Intelligent VirtualAssistants (IVAs) is described herein. It is not necessary for the IVAto understand every possible word in order to understand a user'sintention in a query. Computational overhead of unbounded LMs and datamodels will increase understanding latency. However, some words, such asthose referring to products or services, are important to understand.

Therefore it is desirable to monitor user communication channels for newterminology that should be understood. Personal communication texts suchas emails, instant messaging, and social media are particularlychallenging due to their open vocabulary that continuously grows. Thereare constantly new products, applications, devices, terminology, slangand abbreviations being created and used within such communicationchannels. In order to deal with the evolving nature of internet languagein a timely fashion, automated methods to detect high value words forinsertion into LMs and data models are needed. Conversely, words thatfall out of use, such as discontinued products or cultural referencesthat are no longer popular, should be removed to maintain the size ofmodels and speed traversals through them.

Herein a system and method to detect and prioritize insertions of newterminology into language and data models. Also provided herein are asystem and method to prioritize removal of unused terms from such modelsto limit their growth and searching latency. In order to discover newterms to add to the IVA's knowledge, several content streams may bemonitored. For example, one stream may be trending topics in socialmedia platforms. These may originate from Twitter, Facebook, Pinterestor similar sites where users are actively communicating around topics.As topics gain popularity, they begin to “trend” by rising to the top ofthe subjects or topics that people are communicating about.

For example, during the holiday season there are new products such astoys and electronic devices that are released to take advantage of theseasonal increase in consumer spending. Suppose one such product is anew smart phone device such as the Google Pixel. When the device isannounced or released, there is a sudden emergence in conversationsaround the topic of the Pixel, where before the term was not related tothe electronics domain at all, or may not have even existed if it is anoriginal name. By monitoring trending topics we will observe a suddenappearance of an unknown term, or a term that is not previouslyassociated to the context it appears in.

In a second example, suppose a tropical storm has appeared and is makinglandfall. Tropical storms are commonly named and if the storm isexpected to have a large impact on a populated area many news sites and,therefore, social media sites will experience a sudden spike inconversations around this new name. For IVAs in the travel domain, itwill be helpful that these events are understood quickly as manytravelers will begin to ask about disruptions caused by the storm.

Additional content streams can be customer e-mail and live chattranscripts, or any other form of customer to company communicationchannels. For weather occurrences, feeds such as the NOAA and WeatherChannel can be monitored. News sites and aggregates of new feeds canalso be ingested. From these sources without the construct of trends,terms can be counted over a sliding time window such as a day or week tocreate a set of trending terms.

Regardless of the source, when new terminology appears in trends wefirst consult the existing LM and data models used by the IVA. If theterm is unknown to the LM it must be prioritized for addition. Thisprioritization can be based on any predetermined characteristic, e.g.,frequency, topic, or source. For example, a frequency chart of mentionsacross multiple channels/source may be populated to determine theprevalence of the new term. For example, if a new term is only trendingwithin a specific source such as Twitter, it may refer to some isolatedphenomenon that may not be as important to be known by the IVA.

On the other hand, a new term may be important for the IVA to know tofacilitate handling of incoming customer communication. An example ofsuch phenomenon was the volcanic events that occurred in Iceland in2010, which caused an ash cloud that grounded air transportation inEurope and caused a spike in the usage of terms such as “ash” and“volcano” that were previously very low ranked in IVAs for airlines. Inthe case of a tropical storm name or highly anticipated new product, theterm might have been previously heard by the IVA (such as tropical stormname “Michael”), the term should be temporarily highly ranked acrossmultiple sources, i.e., much higher and in a different way than thegiven name “Michael” might previously have been encountered in theexisting ontology. That is, the context for an existing term haschanged.

We can use this coverage of trending terms across multiple sources, e.g.external to the IVA or in conjunction with the IVA, to inform the usageof terms and to prioritize their addition to or usage in the datamodel/language model. For example, the frequency with which a termappears, context, and/or threshold counts can factor in theprioritization/addition of a term. In one aspect, a human could provideinput for retraining the model based on new terms, includingrecommending a new term's priority and how that term should be deployedto the IVA. In the inverse, a term could be removed or priority reducedbased on these factors, for example, when the volcano eruption is nolonger a factor in air travel or the hurricane has passed, as discussedfurther below.

In some cases the term will be known, but the context it appears in isnew. In the above Google Pixel example, the word “pixel” may alreadyappear in the IVA's vocabulary, but in its ontology it is used fordisplay or camera resolution. To detect usage, we can create embeddingmodels from sliding windows of time using the text from the variousinput sources. If the existing term becomes embedded along with wordsthat were not previously associated with the term, such as “iPhone”,“Apple”, or “Google” in the case of the Pixel, we can determine that thenew usage of the existing term indicates a new object. In these casesthe ontology or other data models will need to be updated to reflect thealternative usage and this update will be prioritized the same way as anew term. Once terms cross a predefined threshold, which is adjustablebased on the availability of human annotators, they are given to a humanalong with example context to be added to the data models and/or LMs.This process of the model selecting its own training data is a specialcase of HITL known as Active Learning [5]. The human will then add theterm to the ontology or update the ontology to reflect the new usage ofthe existing term. For new terms they will need to be added to the LMsas well so that speech recognition engines will be able to decode theword successfully. A flow chart for an exemplary implementation isprovided in FIG. 3 .

For an IVA, how a new term is being embedded can be studied. Forexample, if we look at previous versions of a word lattice or wordembedding model, new text can be fed into the model to see how existingterms are being embedded with respect to new pairs or strings of terms.New usage of a term can therefore be identified and its newmeaning/usage incorporated into the ontology/language model. This allowsfor the new usage to be disambiguated based on context. For example, theterm can be updated in the ontology and its meaning updated. Itsappearance in the language model/word lattice or word embedding modelcan therefore be changed to reflect the updated usage.

While monitoring streams of trending topics and terminology from varioussources, a list of known vocabulary is maintained. This list reflectsall terms known to the IVA through the LMs and data models. Each term inthe vocabulary is associated to a timestamp of last mention. When a termis encountered in the input streams and it exists in the vocabulary, thetimestamp associated to the term is updated. If a product isdiscontinued or a tropical storm passes over, we would expect the usageof such terms will diminish over time. Once a term has not beenmentioned longer than some tunable cutoff period, over one year forexample, it will be deleted from the LMs and data models of the IVAcausing the IVA to “forget” the unused term. In this way terms that havea lifespan are not maintained in the LMs and data models, which willprevent unbounded growth and steadily increasing time to search andtraverse the models. As the pruning requires no human intervention itcan be done automatically, however human review can be used if desiredto approve the modifications.

According to principles described herein, the time from when a new termenters use and its adoption by an IVA (or other language based system)can be reduced, thus causing an improvement in the functioning such adevice.

According to principle described herein, a human may be involved indetermining thresholds by which the system may then run automaticallyfor addition of trending terms and removal of decreasing terms. In thecase of addition, a human in the loop may improve retraining based onnew terms because a human can provide context for use in retraining themodel and recommend priority. Such human activity in conjunction withthe automated system of language model and word embeddings describedabove, can increase the speed with by which the automated models can beretrained to account for trending and declining terms.

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentinvention. Thus, the breadth and scope of the present invention shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A non-transitory computer readable mediumcomprising instructions that, when executed by a processor of aprocessing system, cause the processing system to perform a method, themethod comprising: receiving text from a plurality of platforms;determining whether the text includes a term in a stored data model;identifying if a term that does exist in the data model appears in a newcontext; passing the term in the new context to a human fordetermination if the term should be added to a training example in thenew context for retraining the data model; if the term does not appearin the new context, checking the term for frequency of use in a knowncontext and adding the term in the known context to the training examplewith a new priority if the frequency has reached a predeterminedthreshold; recompiling the language model based on the term in context;and adopting the recompiled language model into an interactive virtualassistant.